OpenRaiser/Intern-Atlas huggingface dataset:
Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks
Venue: arXiv cs.NE 2025
Year: 2025

Biological neurons exhibit diverse temporal spike patterns, which are believed to support efficient, robust, and adaptive neural information processing. While models such as Izhikevich can replicate a wide range of these firing dynamics, their complexity poses challenges for directly integrating them into scalable spiking neural networks (SNN) training pipelines. In this work, we propose two probabilistically driven, input-level temporal spike transformations: Poisson-Burst and Delayed-Burst that introduce biologically inspired temporal variability directly into standard Leaky Integrate-and-Fire (LIF) neurons. This enables scalable training and systematic evaluation of how spike timing dynamics affect privacy, generalization, and learning performance. Poisson-Burst modulates burst occurrence based on input intensity, while Delayed-Burst encodes input strength through burst onset timing. Through extensive experiments across multiple benchmarks, we demonstrate that Poisson-Burst maintains competitive accuracy and lower resource overhead while exhibiting enhanced privacy robustness against membership inference attacks, whereas Delayed-Burst provides stronger privacy protection at a modest accuracy trade-off. These findings highlight the potential of biologically grounded temporal spike dynamics in improving the privacy, generalization and biological plausibility of neuromorphic learning systems.
Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
Venue: arXiv cs.CY 2025
Year: 2025

A key value proposition of machine learning is generalizability: the same methods and model architecture should be able to work across different domains and different contexts. While powerful, this generalization can sometimes go too far, and miss the importance of the specifics. In this work, we look at how fair machine learning has often treated as interchangeable the identity axis along which discrimination occurs. In other words, racism is measured and mitigated the same way as sexism, as ableism, as ageism. Disciplines outside of computer science have pointed out both the similarities and differences between these different forms of oppression, and in this work we draw out the implications for fair machine learning. While certainly not all aspects of fair machine learning need to be tailored to the specific form of oppression, there is a pressing need for greater attention to such specificity than is currently evident. Ultimately, context specificity can deepen our understanding of how to build more fair systems, widen our scope to include currently overlooked harms, and, almost paradoxically, also help to narrow our scope and counter the fear of an infinite number of group-specific methods of analysis.
LSVG: Language-Guided Scene Graphs with 2D-Assisted Multi-Modal Encoding for 3D Visual Grounding
Venue: arXiv cs.CV 2025
Year: 2025

3D visual grounding aims to localize the unique target described by natural languages in 3D scenes. The significant gap between 3D and language modalities makes it a notable challenge to distinguish multiple similar objects through the described spatial relationships. Current methods attempt to achieve cross-modal understanding in complex scenes via a target-centered learning mechanism, ignoring the modeling of referred objects. We propose a novel 3D visual grounding framework that constructs language-guided scene graphs with referred object discrimination to improve relational perception. The framework incorporates a dual-branch visual encoder that leverages pre-trained 2D semantics to enhance and supervise the multi-modal 3D encoding. Furthermore, we employ graph attention to promote relationship-oriented information fusion in cross-modal interaction. The learned object representations and scene graph structure enable effective alignment between 3D visual content and textual descriptions. Experimental results on popular benchmarks demonstrate our superior performance compared to state-of-the-art methods, especially in handling the challenges of multiple similar distractors.
Natural Language Generation in Healthcare: A Review of Methods and Applications
Venue: arXiv cs.CL 2025
Year: 2025

Natural language generation (NLG) is the key technology to achieve generative artificial intelligence (AI). With the breakthroughs in large language models (LLMs), NLG has been widely used in various medical applications, demonstrating the potential to enhance clinical workflows, support clinical decision-making, and improve clinical documentation. Heterogeneous and diverse medical data modalities, such as medical text, images, and knowledge bases, are utilized in NLG. Researchers have proposed many generative models and applied them in a number of healthcare applications. There is a need for a comprehensive review of NLG methods and applications in the medical domain. In this study, we systematically reviewed 113 scientific publications from a total of 3,988 NLG-related articles identified using a literature search, focusing on data modality, model architecture, clinical applications, and evaluation methods. Following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, we categorize key methods, identify clinical applications, and assess their capabilities, limitations, and emerging challenges. This timely review covers the key NLG technologies and medical applications and provides valuable insights for future studies to leverage NLG to transform medical discovery and healthcare.
QStore: Quantization-Aware Compressed Model Storage
Venue: arXiv cs.DB 2025
Year: 2025

Modern applications commonly leverage large, multi-modal foundation models. These applications often feature complex workflows that demand the storage and usage of similar models in multiple precisions. A straightforward approach is to maintain a separate file for each model precision (e.g., INT8, BF16), which is indeed the approach taken by many model providers such as HuggingFace and Ollama. However, this approach incurs excessive storage costs since a higher precision model (e.g., BF16) is a strict superset of a lower precision model (e.g., INT8) in terms of information. Unfortunately, simply maintaining only the higher-precision model and requiring every user to dynamically convert the model precision is not desirable because every user of lower precision models must pay the cost for model download and precision conversion. In this paper, we present QStore, a unified, lossless compression format for simultaneously storing a model in two (high and low) precisions efficiently. Instead of storing low-precision and high-precision models separately, QStore stores low-precision model and only the residual information needed to reconstruct high-precision models. The size of residual information is significantly smaller than the original high-precision models, thus achieving high savings in storage cost. Moreover, QStore does not compromise the speed of model loading. The low-precision models can be loaded quickly just like before. The high-precision models can also be reconstructed efficiently in memory by merging low-precision data and the residual with QStore's lightweight decoding logic. We evaluate QStore for compressing multiple precisions of popular foundation models, and show that QStore reduces overall storage footprint by up to 2.2x (45% of the original size) while enabling up to 1.7x and 1.8x faster model saving and loading versus existing approaches.
An Empirical Study of OpenAI API Discussions on Stack Overflow
Venue: arXiv cs.SE 2025
Year: 2025

The rapid advancement of large language models (LLMs), represented by OpenAI's GPT series, has significantly impacted various domains such as natural language processing, software development, education, healthcare, finance, and scientific research. However, OpenAI APIs introduce unique challenges that differ from traditional APIs, such as the complexities of prompt engineering, token-based cost management, non-deterministic outputs, and operation as black boxes. To the best of our knowledge, the challenges developers encounter when using OpenAI APIs have not been explored in previous empirical studies. To fill this gap, we conduct the first comprehensive empirical study by analyzing 2,874 OpenAI API-related discussions from the popular Q&A forum Stack Overflow. We first examine the popularity and difficulty of these posts. After manually categorizing them into nine OpenAI API-related categories, we identify specific challenges associated with each category through topic modeling analysis. Based on our empirical findings, we finally propose actionable implications for developers, LLM vendors, and researchers.
SMMT: Siamese Motion Mamba with Self-attention for Thermal Infrared Target Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Thermal infrared (TIR) object tracking often suffers from challenges such as target occlusion, motion blur, and background clutter, which significantly degrade the performance of trackers. To address these issues, this paper pro-poses a novel Siamese Motion Mamba Tracker (SMMT), which integrates a bidirectional state-space model and a self-attention mechanism. Specifically, we introduce the Motion Mamba module into the Siamese architecture to ex-tract motion features and recover overlooked edge details using bidirectional modeling and self-attention. We propose a Siamese parameter-sharing strate-gy that allows certain convolutional layers to share weights. This approach reduces computational redundancy while preserving strong feature represen-tation. In addition, we design a motion edge-aware regression loss to improve tracking accuracy, especially for motion-blurred targets. Extensive experi-ments are conducted on four TIR tracking benchmarks, including LSOTB-TIR, PTB-TIR, VOT-TIR2015, and VOT-TIR 2017. The results show that SMMT achieves superior performance in TIR target tracking.
LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling
Venue: arXiv cs.CR 2025
Year: 2025

Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. To fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases.
A Framework to Prevent Biometric Data Leakage in the Immersive Technologies Domain
Venue: arXiv cs.CR 2025
Year: 2025

Doubtlessly, the immersive technologies have potential to ease people's life and uplift economy, however the obvious data privacy risks cannot be ignored. For example, a participant wears a 3D headset device which detects participant's head motion to track the pose of participant's head to match the orientation of camera with participant's eyes positions in the real-world. In a preliminary study, researchers have proved that the voice command features on such headsets could lead to major privacy leakages. By analyzing the facial dynamics captured with the motion sensors, the headsets suffer security vulnerabilities revealing a user's sensitive speech without user's consent. The psychography data (such as voice command features, facial dynamics, etc.) is sensitive data and it should not be leaked out of the device without users consent else it is a privacy breach. To the best of our literature review, the work done in this particular research problem is very limited. Motivated from this, we develop a simple technical framework to mitigate sensitive data (or biometric data) privacy leaks in immersive technology domain. The performance evaluation is conducted in a robust way using six data sets, to show that the proposed solution is effective and feasible to prevent this issue.
Evaluating Performance Consistency in Competitive Programming: Educational Implications and Contest Design Insights
Venue: arXiv cs.CY 2025
Year: 2025

Competitive programming (CP) contests are often treated as interchangeable proxies for algorithmic skill, yet the extent to which results at lower contest tiers anticipate performance at higher tiers, and how closely any tier resembles the ubiquitous online-contest circuit, remains unclear. We analyze ten years (2015--2024) of International Collegiate Programming Contest (ICPC) standings, comprising five long-running superregional championships (Africa \& Arab, Asia East, Asia West, North America, and Northern Eurasia), associated local regionals of North America and Northern Eurasia, and the World Finals. For 366 World Finalist teams (2021--2024) we augment the dataset with pre-contest Codeforces ratings. Pairwise rank alignment is measured with Kendall's $τ$. Overall, superregional ranks predict World Final ranks only moderately (weighted $τ=0.407$), but regional-to-superregional consistency varies widely: Northern Eurasia exhibits the strongest alignment ($τ=0.521$) while Asia West exhibits the weakest ($τ=0.188$). Internal consistency within a region can exceed its predictive value for Worlds -- e.g., Northern Eurasia and North America regionals vs. superregionals ($τ=0.666$ and $τ=0.577$, respectively). Codeforces ratings correlate more strongly with World Final results ($τ=0.596$) than any single ICPC tier, suggesting that high-frequency online contests capture decisive skill factors that many superregional sets miss. We argue that contest organizers can improve both fairness and pedagogical value by aligning problem style and selection rules with the formats that demonstrably differentiate teams, in particular the Northern-Eurasian model and well-curated online rounds. All data, scripts, and additional analyses are publicly released to facilitate replication and further study.
Global Hash Tables Strike Back! An Analysis of Parallel GROUP BY Aggregation
Venue: arXiv cs.DB 2025
Year: 2025

Efficiently computing group aggregations (i.e., GROUP BY) on modern architectures is critical for analytic database systems. Hash-based approaches in today's engines predominantly use a partitioned approach, in which incoming data is partitioned by key values so that every row for a particular key is sent to the same thread. In this paper, we revisit a simpler strategy: a fully concurrent aggregation technique using a shared hash table. While approaches using general-purpose concurrent hash tables have generally been found to perform worse than partitioning-based approaches, we argue that the key ingredient is customizing the concurrent hash table for the specific task of group aggregation. Through experiments on synthetic workloads (varying key cardinality, skew, and thread count), we demonstrate that in morsel-driven systems, a purpose-built concurrent hash table can match or surpass partitioning-based techniques. We also analyze the operational characteristics of both techniques, including resizing costs and memory pressure. In the process, we derive practical guidelines for database implementers. Overall, our analysis indicates that fully concurrent group aggregation is a viable alternative to partitioning.
Towards Large-scale Generative Ranking
Venue: arXiv cs.IR 2025
Year: 2025

Generative recommendation has recently emerged as a promising paradigm in information retrieval. However, generative ranking systems are still understudied, particularly with respect to their effectiveness and feasibility in large-scale industrial settings. This paper investigates this topic at the ranking stage of Xiaohongshu's Explore Feed, a recommender system that serves hundreds of millions of users. Specifically, we first examine how generative ranking outperforms current industrial recommenders. Through theoretical and empirical analyses, we find that the primary improvement in effectiveness stems from the generative architecture, rather than the training paradigm. To facilitate efficient deployment of generative ranking, we introduce GenRank, a novel generative architecture for ranking. We validate the effectiveness and efficiency of our solution through online A/B experiments. The results show that GenRank achieves significant improvements in user satisfaction with nearly equivalent computational resources compared to the existing production system.
Beyond Task Performance: Human Experience in Human-Robot Collaboration
Venue: arXiv cs.RO 2025
Year: 2025

Human interaction experience plays a crucial role in the effectiveness of human-machine collaboration, especially as interactions in future systems progress towards tighter physical and functional integration. While automation design has been shown to impact task performance, its influence on human experience metrics such as flow, sense of agency (SoA), and embodiment remains underexplored. This study investigates how variations in automation design affect these psychological experience measures and examines correlations between subjective experience and physiological indicators. A user study was conducted in a simulated wood workshop, where participants collaborated with a lightweight robot under four automation levels. The results of the study indicate that medium automation levels enhance flow, SoA and embodiment, striking a balance between support and user autonomy. In contrast, higher automation, despite optimizing task performance, diminishes perceived flow and agency. Furthermore, we observed that grip force might be considered as a real-time proxy of SoA, while correlations with heart rate variability were inconclusive. The findings underscore the necessity for automation strategies that integrate human- centric metrics, aiming to optimize both performance and user experience in collaborative robotic systems
Estimating Causal Effects in Networks with Cluster-Based Bandits
Venue: arXiv cs.LG 2025
Year: 2025

The gold standard for estimating causal effects is randomized controlled trial (RCT) or A/B testing where a random group of individuals from a population of interest are given treatment and the outcome is compared to a random group of individuals from the same population. However, A/B testing is challenging in the presence of interference, commonly occurring in social networks, where individuals can impact each others outcome. Moreover, A/B testing can incur a high performance loss when one of the treatment arms has a poor performance and the test continues to treat individuals with it. Therefore, it is important to design a strategy that can adapt over time and efficiently learn the total treatment effect in the network. We introduce two cluster-based multi-armed bandit (MAB) algorithms to gradually estimate the total treatment effect in a network while maximizing the expected reward by making a tradeoff between exploration and exploitation. We compare the performance of our MAB algorithms with a vanilla MAB algorithm that ignores clusters and the corresponding RCT methods on semi-synthetic data with simulated interference. The vanilla MAB algorithm shows higher reward-action ratio at the cost of higher treatment effect error due to undesired spillover. The cluster-based MAB algorithms show higher reward-action ratio compared to their corresponding RCT methods without sacrificing much accuracy in treatment effect estimation.
On the Vulnerability of Underwater Magnetic Induction Communication
Venue: arXiv cs.CR 2025
Year: 2025

Typical magnetic induction (MI) communication is commonly considered a secure underwater wireless communication (UWC) technology due to its non-audible and non-visible nature compared to acoustic and optical UWC technologies. However, vulnerabilities in communication systems inevitably exist and may lead to different types of attacks. In this paper, we investigate the eavesdropping attack in underwater MI communication to quantitatively measure the system's vulnerability under this attack. We consider different potential eavesdropping configuration setups based on the positions and orientations of the eavesdropper node to investigate how they impact the received voltage and secrecy at the legitimate receiver node. To this end, we develop finite-element-method-based simulation models for each configuration in an underwater environment and evaluate the received voltage and the secrecy capacity against different system parameters such as magnetic flux, magnetic flux density, distance, and orientation sensitivity. Furthermore, we construct an experimental setup within a laboratory environment to replicate the simulation experiments. Both simulation and lab experimental confirm the susceptibility of underwater MI communication to eavesdropping attacks. However, this vulnerability is highly dependent on the position and orientation of the coil between the eavesdropper and the legitimate transmitter. On the positive side, we also observe a unique behavior in the received coil reception that might be used to detect malicious node activities in the vicinity, which might lead to a potential security mechanism against eavesdropping attacks.
Facilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Multi-agent autonomous systems (MAS) are better at addressing challenges that spans across multiple domains than singular autonomous agents. This holds true within the field of software engineering (SE) as well. The state-of-the-art research on MAS within SE focuses on integrating LLMs at the core of autonomous agents to create LLM-based multi-agent autonomous (LMA) systems. However, the introduction of LMA systems into SE brings a plethora of challenges. One of the major challenges is the strategic allocation of tasks between humans and the LMA system in a trustworthy manner. To address this challenge, a RACI-based framework is proposed in this work in progress article, along with implementation guidelines and an example implementation of the framework. The proposed framework can facilitate efficient collaboration, ensure accountability, and mitigate potential risks associated with LLM-driven automation while aligning with the Trustworthy AI guidelines. The future steps for this work delineating the planned empirical validation method are also presented.
Bridging Geometry-Coherent Text-to-3D Generation with Multi-View Diffusion Priors and Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Score Distillation Sampling (SDS) leverages pretrained 2D diffusion models to advance text-to-3D generation but neglects multi-view correlations, being prone to geometric inconsistencies and multi-face artifacts in the generated 3D content. In this work, we propose Coupled Score Distillation (CSD), a framework that couples multi-view joint distribution priors to ensure geometrically consistent 3D generation while enabling the stable and direct optimization of 3D Gaussian Splatting. Specifically, by reformulating the optimization as a multi-view joint optimization problem, we derive an effective optimization rule that effectively couples multi-view priors to guide optimization across different viewpoints while preserving the diversity of generated 3D assets. Additionally, we propose a framework that directly optimizes 3D Gaussian Splatting (3D-GS) with random initialization to generate geometrically consistent 3D content. We further employ a deformable tetrahedral grid, initialized from 3D-GS and refined through CSD, to produce high-quality, refined meshes. Quantitative and qualitative experimental results demonstrate the efficiency and competitive quality of our approach.